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MODELLING AND MONITORING MATERNAL MORTALITY RATE IN SOUTH SUDAN

Part of: Linear inference, regression Applications Inference from stochastic processes

Published online by Cambridge University Press:  11 September 2020

GABRIEL MAKUEI Open the ORCID record for GABRIEL MAKUEI [Opens in a new window]
GABRIEL MAKUEI*
Affiliation:
Department of Mathematics and Statistics, College of Science, Dr. John Garang Memorial University of Science and Technology, Bor Jonglei State, South Sudan
*
e-mail: Leek123deng@gmail.com, gabriel.makuei@rmit.edu.au
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Abstract

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Keywords

maternal mortality ratetime series analysisregression models

MSC classification

Primary: 62P10: Applications to biology and medical sciences
Secondary: 62J02: General nonlinear regression 62M10: Time series, auto-correlation, regression, etc.
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 103 , Issue 2 , April 2021 , pp. 343 - 345
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Copyright
© 2020 Australian Mathematical Publishing Association Inc.

Footnotes

Thesis submitted to RMIT University in January 2019; degree approved on 3 April 2019; senior supervisor Mali Abdollahian, associate supervisor Kaye Marion.

References

Makuei, G., Abdollahian, M. and Marion, K., ‘Modelling maternal mortality rates in South Sudan’, Proceedings of the 15th International Conference on Information and Knowledge Engineering (IKE’16) (CSREA, Sterling, VA, 2017), 107-112.Google Scholar
Makuei, G., Abdollahian, M. and Marion, K., ‘Optimal profile limits for maternal mortality rate (MMR) in South Sudan’, BMC Pregnancy Childb. 18(12) (2018), Article ID 278, 12 pages.Google Scholar
Makuei, G., Abdollahian, M. and Marion, K., ‘Optimal profile limits for maternal mortality rate (MMR) influenced by haemorrhage and unsafe abortion in South Sudan’, J. Pregnancy 2020 (2020), Article ID 2793960, 13 pages.CrossRefGoogle ScholarPubMed